In this Tech Brief we present some studies of the use of the component mode synthesis* and the subspace iteration method for obtaining eigenvalue/eigenvector solutions of large finite element models. The equations (1) to (4) of the March 30, 2013 Tech Brief are used as the component mode synthesis steps to obtain approximate frequencies and mode shapes of the dynamic system. However, these equations are also the first iteration of the Bathe subspace iteration method. Then iterating using

with obtained from the component mode synthesis solution, will lead to an accurate solution of the frequencies and mode shapes, see Chapter 11.6.2 of [1], and [2].

We illustrate this point and the possibilities provided in the solution scheme with two analyses.

In the first analysis, we consider the model of the front end of a
tractor, seen in the movie above. This is a nonlinear analysis, in which
the number of equations = 1,214,135, the number of contact equations = 3,546, and the number of frequencies required = 20. The number of iteration vectors = 40. First, we perform the
component mode synthesis (CMS) solution with 10 static constraint mode
vectors and 30 fixed interface vibration mode vectors. This
corresponds to the first step of the subspace iteration. The required
frequencies and mode shape vectors are approximate, in that the errors
in the frequencies are about 10% on average. The
predicted mode shapes are of course even less accurate.

Of course, in practice these errors are unknown. The errors have been
established by also calculating the exact frequencies (to 6 digits)
of the model, by simply continuing the subspace iterations, as given
in Equations (1) to (5) above. The total number
of subspace iterations needed for convergence is given in Table 1
which also lists experiences when more frequencies and mode shapes are
sought.

Table 1 No. of subspace iterations for the front end tractor example; high precision results obtained

In the second example we consider the plasma fusion model, see Figure 1,
also solved earlier,
see also [2] below. Here we are now only interested in the lowest
20 frequencies and mode shapes, solved with 40 iteration vectors. This
model is large because it contains about 5 million equations with
about 700,000 Lagrange multiplier equations imposing contact
conditions. The starting vectors can be established using a small or
large number of static constraint modes, see Table 2. As seen in the
table, the total solution time is not varying by very much. But this may of
course be different in other analyses. The frequencies were solved on a single workstation with two quad-core processors running at 2.4 GHz and with a total of 64 GB of RAM.

Figure 1 Finite element model of a coil and support structure of a plasma fusion device

However, an advantage of the subspace iteration when used as a continuation
of the CMS, is that there is no need to iterate
to obtain a high precision answer, as required for the results referred to in
Table 2. For example, performing another 2 iterations after the
CMS step (using a total of 3 full subspace
iterations) and also allowing for the fixed interface vibration modes only
4 iterations, reduces the solution time as shown in Table 3. In
practice, the CMS step might be performed and
thereafter just a few user-specified subspace iterations are used to improve
the solution result, or to see by how much the solution result is
changed, depending on the size of the problem solved and the
available computational resources. We should note the good accuracy still obtained with fewer iterations, as shown in Figure 2.

While, clearly, experienced users of the CMS approach might get very accurate solutions to the frequencies and mode shapes sought (at a very reasonable cost) of a finite element model, in general the errors in the frequencies and mode shapes are unknown. However, the implementation in ADINA is that CMS can be followed by subspace iterations to give increasingly accurate solutions of frequencies and mode shapes, if required including nonlinear effects, like contact. The implementation of this scheme in ADINA provides users with powerful and flexible options for analyzing large and complex systems in industry.